五种数字素养干预提升识破深度伪造图像能力，最高提升13个百分点。
Designing Effective Digital Literacy Interventions for Boosting Deepfake Discernment
- 通过文本、视觉、游戏等五种方式训练公众识别深度伪造图像。
- 实验显示干预可使识别准确率提升最高13个百分点，且不降低对真实图像的信任。
- 轻量易懂的干预方案适合大规模推广，尤其适用于公众教育场景。
深度伪造图像会削弱公众对机构的信任并影响选举结果，因为人们常难以区分真实与伪造图像。提升数字素养有助于应对这一挑战。本文对比了五种数字素养干预措施的有效性：（1）提供深度伪造常见特征的文本说明；（2）可视化展示这些特征；（3）通过游戏化练习识别深度伪造；（4）通过反复暴露与反馈实现隐性学习；（5）借助AI解释深度伪造生成机制。我们在美国招募了N=1,200名参与者，测试这些干预措施的即时与长期效果。结果显示，这些轻量且易于理解的干预措施可使深度伪造图像识别准确率提升最高达13个百分点，同时保持对真实图像的信任度不变。
原文摘要 · Abstract (English)
Deepfakes images can erode trust in institutions and compromise election outcomes, as people often struggle to discern real images from deepfake images. Improving digital literacy can help address these challenges. Here, we compare the efficacy of five digital literacy interventions to boost people's ability to discern deepfakes: (1) textual guidance on common indicators of deepfakes; (2) visual demonstrations of these indicators; (3) a gamified exercise for identifying deepfakes; (4) implicit learning through repeated exposure and feedback; and (5) explanations of how deepfakes are generated with the help of AI. We conducted an experiment with N=1,200 participants from the United States to test the immediate and long-term effectiveness of our interventions. Our results show that our lightweight, easy-to-understand interventions can boost deepfake image discernment by up to 13 percentage points while maintaining trust in real images.
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